Geological disaster monitoring system based on intelligent hidden danger discrimination

Abstract

The invention relates to the field of geological disaster recognition based on intelligent hidden danger judgment, in particular to a geological disaster monitoring system based on intelligent hidden danger judgment, which comprises an intelligent acquisition module, a displacement analysis module, a soil humidity analysis module, an intelligent hidden danger judgment module and a remote monitoring module, the soil humidity analysis module analyzes the hidden danger of the current geological disaster prone area according to the received soil humidity value, and the displacement analysis module analyzes the mountain displacement change of the current geological disaster prone area and sends the analysis result to the intelligent hidden danger judgment module; the intelligent hidden danger judgment module carries out feature extraction on the current geological disaster and judges the type of the current geological disaster, and the system can adapt to different geological conditions and environment changes and can be widely applied to monitoring and treatment work of various geological disaster prone areas.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster identification based on intelligent hidden danger discrimination, and in particular to a geological disaster monitoring system based on intelligent hidden danger discrimination. Background Art

[0002] Geological disasters pose a serious threat to human life, property and ecological environment. Traditional geological disaster identification methods have gradually exposed many limitations in dealing with increasingly complex geological disaster situations. In terms of hidden danger identification, there is a lack of intelligent analysis methods, and it is impossible to conduct a comprehensive analysis of multi-source data. It is difficult to organically combine factors closely related to geological disasters such as mountain displacement, soil moisture and rainfall for in-depth analysis, so that potential geological disaster hazards cannot be discovered in a timely and accurate manner. In terms of the implementation of governance measures, due to the lack of precision in early monitoring and identification, the governance measures lack pertinence. Usually, only some general measures can be taken, and it is difficult to formulate personalized and efficient governance plans based on specific disaster types, hidden danger levels, etc., which affects the governance effect, causes waste of resources, and cannot effectively reduce the risks brought by geological disasters. In order to overcome these problems and improve the scientificity, accuracy and effectiveness of geological disaster identification, there is an urgent need for a geological disaster monitoring system based on intelligent hidden danger identification to achieve comprehensive monitoring, intelligent identification and precise governance of geological disasters to ensure the safety of people's lives and property and the stability of the ecological environment. Summary of the invention

[0003] In order to solve the technical problems raised by the above-mentioned background technology, the present invention provides a geological disaster monitoring system based on intelligent hidden danger identification.

[0004] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a geological disaster monitoring system based on intelligent hidden danger identification, comprising an intelligent acquisition module, a displacement analysis module, a soil moisture analysis module, an intelligent hidden danger identification module, a remote monitoring module and a data center.

[0005] The intelligent acquisition module deploys various types of sensors in areas prone to geological disasters to collect various types of data information in real time. The specific acquisition process is as follows: Obtain the location information of the current geological disaster prone area and deploy various types of sensors in the current geological disaster prone area. The various types of sensors include displacement sensors, humidity sensors and stress sensors; High-resolution topographic image data is regularly acquired through drones to obtain topographic image data of the current geological disaster-prone areas; the mountain displacement data of the current geological disaster-prone areas is collected in real time through displacement sensors to obtain the mountain displacement values ​​of the geological disaster-prone areas; the acquisition frequency of the humidity sensor is set to collect displacement data information once an hour during the rainy season and once every four hours during the non-rainy season; the soil moisture data of the current geological disaster-prone areas is collected in real time through the humidity sensor to obtain the soil moisture values ​​of the geological disaster-prone areas; the output end of the intelligent acquisition module is connected to the input ends of the displacement analysis module and the soil moisture analysis module, the obtained mountain displacement value is sent to the displacement analysis module, and the soil moisture value and topographic image data are sent to the soil moisture analysis module.

[0006] The displacement analysis module analyzes the displacement changes of the mountains in the current geological disaster-prone areas and sends the analysis results to the intelligent hidden danger identification module. The specific acquisition process is as follows: The input end of the displacement analysis module is connected to the output end of the data center, and the output end of the displacement analysis module is connected to the input end of the intelligent hidden danger identification module. The displacement changes of the current mountain are monitored in real time, and the starting monitoring time point of the mountain displacement is obtained, marked as the initial monitoring time point, and the displacement coordinate point corresponding to the initial monitoring time point is obtained, and it is marked as the initial displacement coordinate point. The monitoring time point of the current system is terminated, and it is marked as the end monitoring time point. The displacement coordinate point corresponding to the end monitoring time point is obtained and marked as the end monitoring time point. The initial monitoring time point and the end monitoring time point are connected to obtain the total monitoring time of the mountain displacement, and then the initial displacement coordinate point and the end displacement coordinate point are connected to obtain the total displacement distance of the mountain. The total monitoring time of the mountain displacement and the total displacement distance of the mountain are proportionally divided into several mountain displacement monitoring time lengths and mountain displacement segment values. Each mountain displacement monitoring time corresponds to a mountain displacement segment value. The mountain displacement segment value is divided by the mountain displacement monitoring time to obtain the displacement change rate, marked as TC. The average of each mountain displacement segment value is calculated to obtain the mountain displacement average value, marked as TG. The mountain displacement values ​​are arranged from large to small to obtain the maximum mountain displacement value and the minimum mountain displacement value. The maximum mountain displacement value and the minimum mountain displacement value are subtracted to obtain the mountain displacement difference value, marked as TP. The current ground stress data is obtained in real time through the stress sensor to obtain the mountain stress value. Different rock and soil components of the current mountain are set, and different rock and soil components and mountain stress values ​​are matched to obtain the rock and soil stress coupling coefficient, marked as TV. The displacement change rate, mountain displacement average value, rock and soil stress coupling coefficient and mountain displacement difference value are calculated using the formula The comprehensive assessment value TK of mountain displacement is obtained, where G1, G2, G3 and G4 are preset proportional coefficients. The preset comprehensive assessment value of mountain displacement of the data center is extracted, and the actual comprehensive assessment value of mountain displacement is compared with the preset comprehensive assessment value of mountain displacement. If the actual comprehensive assessment value of mountain displacement is greater than the preset comprehensive assessment value of mountain displacement, an early warning signal is generated and sent to the corresponding intelligent hidden danger identification module.

[0007] The soil moisture analysis module analyzes the hidden dangers of the current geological disaster-prone areas based on the received soil moisture values. The specific acquisition process is as follows: The input end of the soil moisture analysis module is connected to the output end of the data center, and the rainfall of the mountain on rainy days is monitored in real time to obtain the rainfall at each monitoring time point, and the soil moisture value of the mountain corresponding to each monitoring time point is obtained to obtain the soil moisture value at each monitoring time point. The rainfall at each monitoring time point is matched with the corresponding soil moisture value. The greater the rainfall, the greater the corresponding soil moisture value. A rainfall-humidity associated data group is obtained, and a preset rainfall-humidity associated value of the data center is extracted. Each rainfall-humidity associated value in the rainfall-humidity associated data group is compared with a preset rainfall-humidity associated value, and a rainfall-humidity associated value greater than the preset rainfall-humidity associated value is marked as a rainfall-humidity excess value, and each rainfall-humidity excess value is averaged to obtain a rainfall-humidity average excess value. , extract the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value in the rainfall humidity correlation data group, and make a difference between the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value to obtain the rainfall humidity difference value ; According to the terrain image data obtained, the overall surface area of ​​the current mountain is obtained, marked as SK, the vertical projection area is set to SJ, the mountain slope is SL, and according to the formula Get the actual vegetation coverage area Sa, and then according to the formula The vegetation coverage rate Fc is obtained, and the vegetation coverage characterization value is obtained based on the vegetation coverage area and vegetation coverage rate, which is marked as ; According to the obtained average rainfall humidity excess value, rainfall humidity difference value and vegetation coverage rate, the formula is used to calculate The comprehensive index of geological disaster risk HT is obtained, where L1, L2 and L3 are preset proportional coefficients, j represents the number of the monitoring time point, n represents the total number of monitoring time points, and the preset rainfall and humidity average excess value is set. , set the preset rainfall humidity difference value , extract the preset comprehensive geological hazard risk threshold of the data center, compare the comprehensive geological hazard risk index with the preset comprehensive geological hazard risk threshold, and if the comprehensive geological hazard risk index is greater than the preset comprehensive geological hazard risk threshold, generate an abnormal signal and send it to the intelligent hidden danger identification module.

[0008] The intelligent hidden danger identification module extracts the characteristics of the current geological disasters and determines the current geological disaster type. The specific acquisition process is as follows: The input end of the intelligent hidden danger identification module is connected to the output end of the data center. After receiving the warning signal, the terrain image data monitored by the current drone is obtained, the number and area of ​​cracks in the mountain rock in the terrain image data are extracted, the number and area of ​​each crack are statistically obtained, the corresponding crack areas of each crack number are added up to obtain the total crack area, the preset total crack area of ​​the data center is extracted, and the total crack area is compared with the preset total crack area. If the actual total crack area is greater than the preset total crack area, it is determined that the current situation is a mountain collapse feature, and a collapse command is generated and sent to the corresponding staff mobile phone terminal to alert the surrounding area of ​​the nearby mountain. Install sound and light alarms around the mountain. When receiving abnormal signals, obtain the current amount of sand, gravel and soil accumulation on the hillside through drones, and then obtain the current rainfall on the mountain. The coupling value of disaster-causing factors is obtained based on the analysis of the sand, gravel and soil accumulation and rainfall, and the preset disaster-causing factor coupling threshold of the data center is extracted. The coupling value of disaster-causing factors is compared with the preset disaster-causing factor coupling threshold. If the coupling value of disaster-causing factors is greater than the preset disaster-causing factor coupling threshold, it is judged that the current situation is a debris flow feature, and an emergency command is generated and sent to the sound and light alarm and staff. The sound and light alarm receives the emergency command and issues an alarm reminder. The staff receives the emergency command and obtains the corresponding mountain positioning for maintenance.

[0009] The remote monitoring module uses high-definition cameras to remotely monitor the status of geological disasters in real time and handle mountains with geological hazards. The specific acquisition process is as follows: High-definition cameras are installed around the mountain to obtain real-time camera images from all directions of the mountain, and the camera images are transmitted to the computer screen. The staff monitors the status of the mountain in real time through the camera images. If there is a geological hazard in the mountain, the corresponding mountain is used as the circle point to obtain the location of each fire base within the radius, and the corresponding mountain and the location of each fire base are connected to obtain the various rescue distances. The nearest rescue distance is selected and an emergency command is generated and sent to the corresponding fire base. The fire base receives the emergency command and obtains the corresponding mountain position for rescue.

[0010] Compared with the prior art, the beneficial effect of the present invention is that by deploying various types of sensors including displacement sensors, humidity sensors and stress sensors in areas prone to geological disasters, and combining drones to regularly obtain high-resolution terrain image data, various types of data related to geological disasters can be collected in all directions, overcoming the problem of single and incomplete data of traditional monitoring methods.

[0011] The soil moisture analysis module not only analyzes the relationship between soil moisture and rainfall, but also calculates vegetation coverage related parameters in combination with terrain image data, and then obtains a comprehensive index of geological disaster risk, comprehensively considers the impact of multiple factors on geological disasters, and accurately assesses the degree of hidden dangers. The intelligent hidden danger identification module is based on multi-source data feature extraction, determines the collapse characteristics according to the mountain rock cracks in the terrain image data, and determines the debris flow characteristics according to the sand and gravel soil accumulation and rainfall analysis. It can accurately identify different types of geological disasters and provide a basis for targeted governance, avoiding the misjudgment caused by traditional methods based on experience alone. The modules of the system work closely together, and the data is automatically collected, analyzed and processed, realizing the intelligent process of geological disaster identification. By presetting various thresholds and parameters and adjusting them according to actual conditions, the system can adapt to different geological conditions and environmental changes, and can be widely used in the monitoring and governance of various areas prone to geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0013] Figure 1 It is a principle block diagram of the present invention.

[0014] Figure 2 It is a step diagram of the monitoring method of the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0016] Please refer to Figure 1 As shown, the first aspect of the present invention provides a geological disaster monitoring system based on intelligent hidden danger identification, including an intelligent acquisition module, a displacement analysis module, a soil moisture analysis module, an intelligent hidden danger identification module, a remote monitoring module and a data center.

[0017] The intelligent acquisition module deploys various types of sensors in areas prone to geological disasters to collect various types of data information in real time. The specific acquisition process is as follows: Obtain the location information of the current geological disaster prone area and deploy various types of sensors in the current geological disaster prone area. The various types of sensors include displacement sensors, humidity sensors and stress sensors; Regularly acquire high-resolution topographic image data through drones to obtain topographic image data of the current geological disaster-prone areas; use displacement sensors to collect mountain displacement data in the current geological disaster-prone areas in real time to obtain mountain displacement values ​​in the geological disaster-prone areas; set the acquisition frequency of the humidity sensor to collect displacement data information once an hour during the rainy season and once every four hours during the non-rainy season; use humidity sensors to collect soil moisture data in the current geological disaster-prone areas in real time to obtain soil moisture values ​​in the geological disaster-prone areas; the output end of the intelligent acquisition module is connected to the input end of the displacement analysis module and the soil moisture analysis module, and the obtained mountain displacement value is sent to the displacement analysis module, and the soil moisture value and topographic image data are sent to the soil moisture analysis module. The displacement analysis module analyzes the displacement changes of the mountains in the current geological disaster-prone areas and sends the analysis results to the intelligent hidden danger identification module. The specific acquisition process is as follows: The input end of the displacement analysis module is connected to the output end of the data center, and the output end of the displacement analysis module is connected to the input end of the intelligent hidden danger identification module. The displacement changes of the current mountain are monitored in real time, and the starting monitoring time point of the mountain displacement is obtained, marked as the initial monitoring time point, and the displacement coordinate point corresponding to the initial monitoring time point is obtained, and it is marked as the initial displacement coordinate point. The monitoring time point of the current system is terminated, and it is marked as the end monitoring time point. The displacement coordinate point corresponding to the end monitoring time point is obtained and marked as the end monitoring time point. The initial monitoring time point and the end monitoring time point are connected to obtain the total monitoring time of the mountain displacement, and then the initial displacement coordinate point and the end displacement coordinate point are connected to obtain the total displacement distance of the mountain. The total monitoring time of the mountain displacement and the total displacement distance of the mountain are proportionally divided into several mountain displacement monitoring time lengths and mountain displacement segment values. Each mountain displacement monitoring time corresponds to a mountain displacement segment value. The mountain displacement segment value is divided by the mountain displacement monitoring time to obtain the displacement change rate, marked as TC. The average of each mountain displacement segment value is calculated to obtain the mountain displacement average value, marked as TG. The mountain displacement values ​​are arranged from large to small to obtain the maximum mountain displacement value and the minimum mountain displacement value. The maximum mountain displacement value and the minimum mountain displacement value are subtracted to obtain the mountain displacement difference value, marked as TP. The current ground stress data is obtained in real time through the stress sensor to obtain the mountain stress value. Different rock and soil components of the current mountain are set, and different rock and soil components and mountain stress values ​​are matched to obtain the rock and soil stress coupling coefficient, marked as TV. The displacement change rate, mountain displacement average value, rock and soil stress coupling coefficient and mountain displacement difference value are calculated using the formula The comprehensive assessment value TK of mountain displacement is obtained, where G1, G2, G3 and G4 are preset proportional coefficients. The preset comprehensive assessment value of mountain displacement of the data center is extracted, and the actual comprehensive assessment value of mountain displacement is compared with the preset comprehensive assessment value of mountain displacement. If the actual comprehensive assessment value of mountain displacement is greater than the preset comprehensive assessment value of mountain displacement, an early warning signal is generated and sent to the corresponding intelligent hidden danger identification module.

[0018] The soil moisture analysis module analyzes the hidden dangers of the current geological disaster-prone areas based on the received soil moisture values. The specific acquisition process is as follows: The input end of the soil moisture analysis module is connected to the output end of the data center, and the rainfall of the mountain on rainy days is monitored in real time to obtain the rainfall at each monitoring time point, and the soil moisture value of the mountain corresponding to each monitoring time point is obtained to obtain the soil moisture value at each monitoring time point. The rainfall at each monitoring time point is matched with the corresponding soil moisture value. The greater the rainfall, the greater the corresponding soil moisture value. A rainfall-humidity associated data group is obtained, and a preset rainfall-humidity associated value of the data center is extracted. Each rainfall-humidity associated value in the rainfall-humidity associated data group is compared with a preset rainfall-humidity associated value, and a rainfall-humidity associated value greater than the preset rainfall-humidity associated value is marked as a rainfall-humidity excess value, and each rainfall-humidity excess value is averaged to obtain a rainfall-humidity average excess value. , extract the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value in the rainfall humidity correlation data group, and make a difference between the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value to obtain the rainfall humidity difference value ; According to the terrain image data obtained, the overall surface area of ​​the current mountain is obtained, marked as SK, the vertical projection area is set to SJ, the mountain slope is SL, and according to the formula Get the actual vegetation coverage area Sa, and then according to the formula The vegetation coverage rate Fc is obtained, and the vegetation coverage characterization value is obtained based on the vegetation coverage area and vegetation coverage rate, which is marked as ; According to the obtained average rainfall humidity excess value, rainfall humidity difference value and vegetation coverage rate, the formula is used to calculate The comprehensive index of geological disaster risk HT is obtained, where L1, L2 and L3 are preset proportional coefficients, j represents the number of the monitoring time point, n represents the total number of monitoring time points, and the preset rainfall and humidity average excess value is set. , set the preset rainfall humidity difference value , extract the preset comprehensive geological hazard risk threshold of the data center, compare the comprehensive geological hazard risk index with the preset comprehensive geological hazard risk threshold, and if the comprehensive geological hazard risk index is greater than the preset comprehensive geological hazard risk threshold, generate an abnormal signal and send it to the intelligent hidden danger identification module.

[0019] The intelligent hidden danger identification module extracts the characteristics of the current geological disasters and determines the current geological disaster type. The specific acquisition process is as follows: The input end of the intelligent hidden danger identification module is connected to the output end of the data center. After receiving the warning signal, the terrain image data monitored by the current drone is obtained, the number and area of ​​cracks in the mountain rock in the terrain image data are extracted, the number and area of ​​each crack are statistically obtained, the corresponding crack areas of each crack number are added up to obtain the total crack area, the preset total crack area of ​​the data center is extracted, and the total crack area is compared with the preset total crack area. If the actual total crack area is greater than the preset total crack area, it is determined that the current situation is a mountain collapse feature, and a collapse command is generated and sent to the corresponding staff mobile phone terminal to alert the surrounding area of ​​the nearby mountain. Install sound and light alarms around the mountain. When receiving abnormal signals, obtain the current amount of sand, gravel and soil accumulation on the hillside through drones, and then obtain the current rainfall on the mountain. The coupling value of disaster-causing factors is obtained based on the analysis of the sand, gravel and soil accumulation and rainfall, and the preset disaster-causing factor coupling threshold of the data center is extracted. The coupling value of disaster-causing factors is compared with the preset disaster-causing factor coupling threshold. If the coupling value of disaster-causing factors is greater than the preset disaster-causing factor coupling threshold, it is judged that the current situation is a debris flow feature, and an emergency command is generated and sent to the sound and light alarm and staff. The sound and light alarm receives the emergency command and issues an alarm reminder. The staff receives the emergency command and obtains the corresponding mountain positioning for maintenance.

[0020] The remote monitoring module uses high-definition cameras to remotely monitor the status of geological disasters in real time and handle mountains with geological hazards. The specific acquisition process is as follows: High-definition cameras are installed around the mountain to obtain real-time camera images from all directions of the mountain, and the camera images are transmitted to the computer screen. The staff monitors the status of the mountain in real time through the camera images. If there is a geological hazard in the mountain, the corresponding mountain is used as the circle point to obtain the location of each fire base within the radius, and the corresponding mountain and the location of each fire base are connected to obtain the various rescue distances. The nearest rescue distance is selected and an emergency command is generated and sent to the corresponding fire base. The fire base receives the emergency command and obtains the corresponding mountain position for rescue.

[0021] Reference Figure 2 As shown, the second aspect of the present invention provides a monitoring method of a geological disaster monitoring system based on intelligent hidden danger identification, comprising the following steps: G1: Intelligent collection: Based on the deployment of various types of sensors in areas prone to geological disasters, various types of data information are collected in real time. G2: Displacement analysis: Analyze the displacement changes of slopes in areas prone to geological disasters; G3: Soil moisture analysis: Analyze the hidden dangers of the current geological disaster prone areas based on the received soil moisture values; G4: Intelligent hidden danger identification extracts the characteristics of the current geological disasters and determines the current geological disaster type; G5: Remote monitoring uses high-definition cameras to monitor the status of geological disasters in real time and handle mountains with geological hazards.

[0022] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A geological disaster monitoring system based on intelligent hidden danger identification, comprising an intelligent acquisition module, a displacement analysis module, an intelligent hidden danger identification module, a remote monitoring module and a data center, characterized in that: Also includes a soil moisture analysis module; The soil moisture analysis module analyzes the hidden dangers of the current geological disaster-prone areas, monitors the rainfall of the mountain on rainy days in real time, obtains the rainfall at each monitoring time point, obtains the soil moisture value of the mountain corresponding to each monitoring time point, and matches the rainfall at each monitoring time point with the corresponding soil moisture value. The greater the rainfall, the greater the corresponding soil moisture value. A rainfall-humidity associated data group is obtained, and the preset rainfall-humidity associated value of the data center is extracted. Each rainfall-humidity associated value in the rainfall-humidity associated data group is compared with the preset rainfall-humidity associated value. A rainfall-humidity associated value greater than the preset rainfall-humidity associated value is marked as a rainfall-humidity excess value, and the average of each rainfall-humidity excess value is calculated to obtain the rainfall-humidity average excess value. , extract the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value in the rainfall humidity correlation data group, and make a difference between the maximum rainfall humidity correlation value and the minimum rainfall humidity correlation value to obtain the rainfall humidity difference value ;According to the terrain image data, obtain the overall surface area of ​​the current mountain, mark it as SK, set the vertical projection area as SJ, the mountain slope as SL, according to the formula Get the actual vegetation coverage area Sa, and then according to the formula Get the vegetation coverage rate Fc, and get the vegetation coverage characterization value based on the vegetation coverage area and vegetation coverage rate ; Using the formula The comprehensive geological hazard risk index HT is obtained, where L1, L2 and L3 are preset proportional coefficients. The preset comprehensive geological hazard risk threshold of the data center is extracted, and the comprehensive geological hazard risk index is compared with the preset comprehensive geological hazard risk threshold. If the comprehensive geological hazard risk index is greater than the preset comprehensive geological hazard risk threshold, an abnormal signal is generated and sent to the intelligent hidden danger identification module.

2. The geological disaster monitoring system based on intelligent hidden danger identification according to claim 1 is characterized in that: The displacement analysis module analyzes the displacement changes of the mountains in the current geological disaster-prone areas and sends the analysis results to the intelligent hidden danger identification module. The specific process is as follows: The input end of the displacement analysis module is connected to the output end of the data center, and the output end of the displacement analysis module is connected to the input end of the intelligent hidden danger identification module. The displacement changes of the current mountain are monitored in real time, and the starting monitoring time point of the mountain displacement is obtained, marked as the initial monitoring time point, and the displacement coordinate point corresponding to the initial monitoring time point is obtained, and it is marked as the initial displacement coordinate point. The monitoring time point of the current system is terminated, and it is marked as the end monitoring time point. The displacement coordinate point corresponding to the end monitoring time point is obtained and marked as the end monitoring time point. The initial monitoring time point and the end monitoring time point are connected to obtain the total monitoring time of the mountain displacement, and then the initial displacement coordinate point and the end displacement coordinate point are connected to obtain the total displacement distance of the mountain. The total monitoring time of the mountain displacement and the total displacement distance of the mountain are proportionally divided into several mountain displacement monitoring time-sharing units. The length and segmented value of mountain displacement are obtained by dividing the segmented value of mountain displacement by the monitoring time length of mountain displacement to obtain the displacement change rate, which is marked as TC. The segmented values ​​of each mountain displacement are averaged to obtain the average value of mountain displacement, which is marked as TG. The mountain displacement values ​​are arranged from large to small to obtain the maximum mountain displacement value and the minimum mountain displacement value. The maximum mountain displacement value and the minimum mountain displacement value are subtracted to obtain the mountain displacement difference value, which is marked as TP. The current ground stress data is obtained in real time through the stress sensor to obtain the mountain stress value. Different rock and soil components of the current mountain are set, and different rock and soil components are matched with the mountain stress value to obtain the rock and soil stress coupling coefficient, which is marked as TV. The obtained displacement change rate, mountain displacement average value, rock and soil stress coupling coefficient and mountain displacement difference value are calculated using the formula The comprehensive assessment value TK of mountain displacement is obtained, where G1, G2, G3 and G4 are preset proportional coefficients. The preset comprehensive assessment value of mountain displacement of the data center is extracted, and the actual comprehensive assessment value of mountain displacement is compared with the preset comprehensive assessment value of mountain displacement. If the actual comprehensive assessment value of mountain displacement is greater than the preset comprehensive assessment value of mountain displacement, an early warning signal is generated and sent to the corresponding intelligent hidden danger identification module.

3. The geological disaster monitoring system based on intelligent hidden danger identification according to claim 1 is characterized in that: The intelligent hidden danger identification module extracts the characteristics of the current geological disasters and determines the current geological disaster type. The specific process is as follows: The input end of the intelligent hidden danger identification module is connected to the output end of the data center. After receiving the warning signal, the terrain image data monitored by the current drone is obtained, the number and area of ​​cracks in the mountain rock in the terrain image data are extracted, the number and area of ​​each crack are statistically obtained, the corresponding crack areas of each crack number are added up to obtain the total crack area, the preset total crack area of ​​the data center is extracted, and the total crack area is compared with the preset total crack area. If the actual total crack area is greater than the preset total crack area, it is determined that the current situation is a mountain collapse feature, and a collapse command is generated and sent to the corresponding staff mobile phone terminal to alert the surrounding area of ​​the nearby mountain. Install sound and light alarms around the mountain. When receiving abnormal signals, obtain the current amount of sand, gravel and soil accumulation on the hillside through drones, and then obtain the current rainfall on the mountain. The coupling value of disaster-causing factors is obtained based on the analysis of the sand, gravel and soil accumulation and rainfall, and the preset disaster-causing factor coupling threshold of the data center is extracted. The coupling value of disaster-causing factors is compared with the preset disaster-causing factor coupling threshold. If the coupling value of disaster-causing factors is greater than the preset disaster-causing factor coupling threshold, it is judged that the current situation is a debris flow feature, and an emergency command is generated and sent to the sound and light alarm and staff. The sound and light alarm receives the emergency command and issues an alarm reminder. The staff receives the emergency command and obtains the corresponding mountain positioning for maintenance.

4. The geological disaster monitoring system based on intelligent hidden danger identification according to claim 1 is characterized in that: The remote monitoring module uses high-definition cameras to remotely monitor the status of geological disasters in real time and handle mountains with geological hazards. The specific process is as follows: High-definition cameras are installed around the mountain to obtain real-time camera images from all directions of the mountain, and the camera images are transmitted to the computer screen. The staff monitors the status of the mountain in real time through the camera images. If there is a geological hazard in the mountain, the corresponding mountain is used as the circle point to obtain the location of each fire base within the radius, and the corresponding mountain and the location of each fire base are connected to obtain the various rescue distances. The nearest rescue distance is selected and an emergency command is generated and sent to the corresponding fire base. The fire base receives the emergency command and obtains the corresponding mountain position for rescue.

5. The geological disaster monitoring system based on intelligent hidden danger identification according to claim 1 is characterized in that: The intelligent acquisition module deploys various types of sensors in areas prone to geological disasters to collect various types of data information in real time. The specific process is as follows: Obtain the location information of the current geological disaster prone area and deploy various types of sensors in the current geological disaster prone area. The various types of sensors include displacement sensors, humidity sensors and stress sensors; High-resolution topographic image data is regularly acquired through drones to obtain topographic image data of the current geological disaster-prone areas; the mountain displacement data of the current geological disaster-prone areas is collected in real time through displacement sensors to obtain the mountain displacement values ​​of the geological disaster-prone areas; the acquisition frequency of the humidity sensor is set to collect displacement data information once an hour during the rainy season and once every four hours during the non-rainy season; the soil moisture data of the current geological disaster-prone areas is collected in real time through the humidity sensor to obtain the soil moisture values ​​of the geological disaster-prone areas; the output end of the intelligent acquisition module is connected to the input ends of the displacement analysis module and the soil moisture analysis module, the obtained mountain displacement value is sent to the displacement analysis module, and the soil moisture value and topographic image data are sent to the soil moisture analysis module.

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